U.S. patent application number 15/150581 was filed with the patent office on 2017-11-16 for object storage workflow optimization leveraging storage area network value adds.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to SASIKANTH EDA, JOHN T. OLSON, SANDEEP R. PATIL, SUBHOJIT ROY.
Application Number | 20170329647 15/150581 |
Document ID | / |
Family ID | 60294598 |
Filed Date | 2017-11-16 |
United States Patent
Application |
20170329647 |
Kind Code |
A1 |
EDA; SASIKANTH ; et
al. |
November 16, 2017 |
Object Storage Workflow Optimization Leveraging Storage Area
Network Value Adds
Abstract
A mechanism is provided for optimizing object storage workflow.
A category of a computational algorithm received from a user of a
client device is identified, the category identifying a set of
storage area network (SAN) features that are optimal for executing
the computational algorithm. Features associated with a plurality
of nodes in a plurality of infrastructures in an object storage
architecture are searched for at least one node that has the set of
features identified by the category of the computational algorithm.
Responsive to identifying a node that has the set of features
identified by the category of the computational algorithm, a
determination is made as to whether resources associated with the
node are immediately available. Responsive to the resources
associated with the node being immediately available, the
computational algorithm is issued to the node for execution.
Inventors: |
EDA; SASIKANTH; (ANDHRA
PRADESH, IN) ; OLSON; JOHN T.; (TUCSON, AZ) ;
PATIL; SANDEEP R.; (PUNE, IN) ; ROY; SUBHOJIT;
(PUNE, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
60294598 |
Appl. No.: |
15/150581 |
Filed: |
May 10, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/0635 20130101;
G06F 2206/1012 20130101; G06F 3/061 20130101; G06F 3/067
20130101 |
International
Class: |
G06F 9/50 20060101
G06F009/50; G06F 3/06 20060101 G06F003/06; G06F 3/06 20060101
G06F003/06; G06F 3/06 20060101 G06F003/06 |
Claims
1. A method, in a data processing system, for optimizing object
storage workflow, the method comprising: identifying a category of
a computational algorithm received from a user of a client device,
wherein the category identifies a set of storage area network (SAN)
features that are optimal for executing the computational algorithm
and wherein the set of SAN features comprise one or more of thin
provisioning, tiering, compression, deduplication, Flash
acceleration support, high/low speed disk rpm support,
Active/Active replication, or encryption; searching features
associated with a plurality of nodes in a plurality of
infrastructures in an object storage architecture for at least one
node that has the set of features identified by the category of the
computational algorithm; responsive to identifying a node that has
the set of features identified by the category of the computational
algorithm, determining whether resources associated with the node
are immediately available; and responsive to the resources
associated with the node being immediately available, issuing the
computational algorithm to the node for execution, wherein
execution of the computational algorithm returns results to the
user via the client device.
2. The method of claim 1, wherein the category of the computational
algorithm is predefined and received with the computational
algorithm.
3. The method of claim 1, wherein the category of the computational
algorithm is undefined when the computational algorithm is received
and wherein the category is identified by the method comprising:
parsing the computational algorithm to identify a set of operations
required within the computational algorithm; identifying a class of
each operation in the set of operations using a set of predefined
rules for operations; and determining the category of the
computational algorithm based on the identified classes from a
pre-programmed table of categories and value adds, wherein the
value adds associated with each category are an optimal set of SAN
features.
4. The method of claim 1, further comprising: responsive to the
resources associated with the at least one node failing to be
immediately available, waiting a predetermined time period for the
resources to become available; and responsive to the resources
associated with the at least one node becoming available within the
predetermined time period, issuing the computational algorithm to
the node for execution, wherein execution of the computational
algorithm returns results to the user via the client device.
5. The method of claim 4, further comprising: responsive to the
resources associated with the at least one node failing to become
available within the predetermined time period, identifying at
least one other node that has the set of features identified by the
category of the computational algorithm or substantially has the
set of features identified by the category of the computational
algorithm within a predetermined threshold; responsive to
identifying the at least one other node, determining whether
resources associated with the at least one other node are
immediately available; and responsive to the resources associated
with the at least one other node being immediately available,
issuing the computational algorithm to the node for execution.
6. The method of claim 1, further comprising: responsive to
identifying a plurality of nodes that have the set of features
identified by the category of the computational algorithm, randomly
selecting a node from the plurality of nodes; determining whether
resources associated with the randomly selected node are
immediately available; and responsive to the resources associated
with the randomly selected node being immediately available,
issuing the computational algorithm to the randomly selected node
for execution.
7. The method of claim 6, further comprising: responsive to the
resources associated with the randomly selected node failing to
become available within the predetermined time period, randomly
selecting another node from the plurality of nodes; repeating the
determining of whether resources associated with the randomly
selected node are immediately available until a node within the
plurality of nodes has resources that are immediately available;
and responsive to the resources associated with the randomly
selected node being immediately available, issuing the
computational algorithm to the randomly selected node for
execution.
8. A computer program product comprising a computer readable
storage medium having a computer readable program stored therein,
wherein the computer readable program, when executed on a computing
device, causes the computing device to: identify a category of a
computational algorithm received from a user of a client device,
wherein the category identifies a set of storage area network (SAN)
features that are optimal for executing the computational algorithm
and wherein the set of SAN features comprise one or more of thin
provisioning, tiering, compression, deduplication, Flash
acceleration support, high/low speed disk rpm support,
Active/Active replication, or encryption; search features
associated with a plurality of nodes in a plurality of
infrastructures in an object storage architecture for at least one
node that has the set of features identified by the category of the
computational algorithm; responsive to identifying a node that has
the set of features identified by the category of the computational
algorithm, determine whether resources associated with the node are
immediately available; and responsive to the resources associated
with the node being immediately available, issue the computational
algorithm to the node for execution, wherein execution of the
computational algorithm returns results to the user via the client
device.
9. The computer program product of claim 8, wherein the category of
the computational algorithm is predefined and received with the
computational algorithm.
10. The computer program product of claim 8, wherein the category
of the computational algorithm is undefined when the computational
algorithm is received and wherein the computer readable program
identifies the category by causing the computing device to: parse
the computational algorithm to identify a set of operations
required within the computational algorithm; identify a class of
each operation in the set of operations using a set of predefined
rules for operations; and determine the category of the
computational algorithm based on the identified classes from a
pre-programmed table of categories and value adds, wherein the
value adds associated with each category are an optimal set of SAN
features.
11. The computer program product of claim 8, wherein the computer
readable program further causes the computing device to: responsive
to the resources associated with the at least one node failing to
be immediately available, wait a predetermined time period for the
resources to become available; and responsive to the resources
associated with the at least one node becoming available within the
predetermined time period, issue the computational algorithm to the
node for execution, wherein execution of the computational
algorithm returns results to the user via the client device.
12. The computer program product of claim 11, wherein the computer
readable program further causes the computing device to: responsive
to the resources associated with the at least one node failing to
become available within the predetermined time period, identify at
least one other node that has the set of features identified by the
category of the computational algorithm or substantially has the
set of features identified by the category of the computational
algorithm within a predetermined threshold; responsive to
identifying the at least one other node, determine whether
resources associated with the at least one other node are
immediately available; and responsive to the resources associated
with the at least one other node being immediately available, issue
the computational algorithm to the node for execution.
13. The computer program product of claim 8, wherein the computer
readable program further causes the computing device to: responsive
to identifying a plurality of nodes that have the set of features
identified by the category of the computational algorithm, randomly
select a node from the plurality of nodes; determine whether
resources associated with the randomly selected node are
immediately available; and responsive to the resources associated
with the randomly selected node being immediately available, issue
the computational algorithm to the randomly selected node for
execution.
14. The computer program product of claim 13, wherein the computer
readable program further causes the computing device to: responsive
to the resources associated with the randomly selected node failing
to become available within the predetermined time period, randomly
select another node from the plurality of nodes; repeat the
determining of whether resources associated with the randomly
selected node are immediately available until a node within the
plurality of nodes has resources that are immediately available;
and responsive to the resources associated with the randomly
selected node being immediately available, issue the computational
algorithm to the randomly selected node for execution.
15. An apparatus comprising: a processor; and a memory coupled to
the processor, wherein the memory comprises instructions which,
when executed by the processor, cause the processor to: identify a
category of a computational algorithm received from a user of a
client device, wherein the category identifies a set of storage
area network (SAN) features that are optimal for executing the
computational algorithm and wherein the set of SAN features
comprise one or more of thin provisioning, tiering, compression,
deduplication, Flash acceleration support, high/low speed disk rpm
support, Active/Active replication, or encryption; search features
associated with a plurality of nodes in a plurality of
infrastructures in an object storage architecture for at least one
node that has the set of features identified by the category of the
computational algorithm; responsive to identifying a node that has
the set of features identified by the category of the computational
algorithm, determine whether resources associated with the node are
immediately available; and responsive to the resources associated
with the node being immediately available, issue the computational
algorithm to the node for execution, wherein execution of the
computational algorithm returns results to the user via the client
device.
16. The apparatus of claim 15, wherein the category of the
computational algorithm is predefined and received with the
computational algorithm.
17. The apparatus of claim 15, wherein the category of the
computational algorithm is undefined when the computational
algorithm is received and wherein the instructions identify the
category by causing the processor to: parse the computational
algorithm to identify a set of operations required within the
computational algorithm; identify a class of each operation in the
set of operations using a set of predefined rules for operations;
and determine the category of the computational algorithm based on
the identified classes from a pre-programmed table of categories
and value adds, wherein the value adds associated with each
category are an optimal set of SAN features.
18. The apparatus of claim 15, wherein the instructions further
cause the processor to: responsive to the resources associated with
the at least one node failing to be immediately available, wait a
predetermined time period for the resources to become available;
and responsive to the resources associated with the at least one
node becoming available within the predetermined time period, issue
the computational algorithm to the node for execution, wherein
execution of the computational algorithm returns results to the
user via the client device.
19. The apparatus of claim 18, wherein the instructions further
cause the processor to: responsive to the resources associated with
the at least one node failing to become available within the
predetermined time period, identify at least one other node that
has the set of features identified by the category of the
computational algorithm or substantially has the set of features
identified by the category of the computational algorithm within a
predetermined threshold; responsive to identifying the at least one
other node, determine whether resources associated with the at
least one other node are immediately available; and responsive to
the resources associated with the at least one other node being
immediately available, issue the computational algorithm to the
node for execution.
20. The apparatus of claim 15, wherein the instructions further
cause the processor to: responsive to identifying a plurality of
nodes that have the set of features identified by the category of
the computational algorithm, randomly select a node from the
plurality of nodes; determine whether resources associated with the
randomly selected node are immediately available; and responsive to
the resources associated with the randomly selected node being
immediately available, issue the computational algorithm to the
randomly selected node for execution.
Description
BACKGROUND
[0001] The present application relates generally to an improved
data processing apparatus and method and more specifically to
mechanisms for object storage workflow optimization leveraging
storage area network virtualization value adds.
[0002] Traditionally, object storage is used for backup, archival,
data mining, searching, analytics, and the like. FIG. 1 depicts an
example of a traditional object storage architecture. Traditional
object storage architecture 100 comprises two diverse
infrastructures 102 and 112 that are accessible by client devices
120 and 122 via load balancer 124. Each of infrastructures 102 and
112 further comprise two node groups. The first node groups 104 and
114 comprise proxy nodes 104a-104n and 114a-114n that are used for
distributed load handling/request handling from client devices 120
and 122 into the storage namespace. The second node groups 106 and
116, i.e. the storage namespace, comprises storage nodes 106a-106n
and 116a-116n that are responsible for writing to the disks or
storage subsystems and, in this illustrative architecture, purely
serves as a storage unit repository. However, in order to analyze
or extract any meaningful information from raw data retrieved from
the storage nodes 106a-106n and 116a-116n in second node groups 106
and 116, the data must be sent back to client 120 and 122 or to an
additional client 126 or compute node 128 for analysis.
[0003] With the evolution of embedded compute infrastructures with
built-in object storage architecture, computation utilizing the
data stored in these compute infrastructures is offloaded to
storage units instead of using a traditional client device for
computation purposes. FIG. 2 depicts an example of an embedded
compute engine in an object storage (Storlet) architecture. As with
the architecture shown in FIG. 1, storlet architecture 200 of FIG.
2 comprises two diverse infrastructures 202 and 212 that are
accessible by client devices 220 and 222 via load balancer 224.
Each of infrastructures 202 and 212 further comprise two node
groups. The first node groups 204 and 214 comprise proxy nodes
204a-204n and 214a-214n that are used for distributed load
handling/request handling from client devices 220 and 222 into the
storage namespace. The second node groups 206 and 216, i.e. the
storage namespace, comprises storage nodes 206a-206n and 216a-216n
that are responsible for writing to the disks or storage
subsystems.
[0004] However, in addition to the common infrastructure, storlet
architecture 200 also comprises software engines 208 and 218 as
shown within second node groups 206 and 216, respectively. In an
alternative embodiment, software engines 208 and 218 may reside
within first node groups 204 and 214. Utilizing software engines
208 and 218, any computation or analysis required by client device
220 or 222 may be implemented by software engine 208 or 218.
However, a user of client devices 220 and 222 has to frame
computational algorithm to perform the computation or analysis and
has to deploy or pass the computational algorithm to software
engine 208 or 218 at the time of the original request. Then
software engine 208 or 218 sends the results of the computation
back to the requesting user of client device 220 or 222. Therefore,
storlet architecture 200 differs from the traditional object
storage architecture 100 of FIG. 1 in that, storlet architecture
200 does not require any additional client or compute node to
perform computation or analysis of the data. That is, second node
groups 206 and 216 act as compute nodes and return any results back
to the user.
SUMMARY
[0005] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described herein in
the Detailed Description. This Summary is not intended to identify
key factors or essential features of the claimed subject matter,
nor is it intended to be used to limit the scope of the claimed
subject matter.
[0006] In one illustrative embodiment, a method, in a data
processing system, is provided for optimizing object storage
workflow. The illustrative embodiment identifies a category of a
computational algorithm received from a user of a client device. In
the illustrative embodiment, the category identifies a set of
storage area network (SAN) features that are optimal for executing
the computational algorithm. In the illustrative embodiment, the
set of SAN features comprise one or more of thin provisioning,
tiering, compression, deduplication, Flash acceleration support,
high/low speed disk rpm support, Active/Active replication, or
encryption. The illustrative embodiment searches features
associated with a plurality of nodes in a plurality of
infrastructures in an object storage architecture for at least one
node that has the set of features identified by the category of the
computational algorithm. The illustrative embodiment determines
whether resources associated with the node are immediately
available in response to identifying a node that has the set of
features identified by the category of the computational algorithm.
The illustrative embodiment issues the computational algorithm to
the node for execution in response to the resources associated with
the node being immediately available. In the illustrative
embodiment, execution of the computational algorithm returns
results to the user via the client device.
[0007] In other illustrative embodiments, a computer program
product comprising a computer useable or readable medium having a
computer readable program is provided. The computer readable
program, when executed on a computing device, causes the computing
device to perform various ones of, and combinations of, the
operations outlined above with regard to the method illustrative
embodiment.
[0008] In yet another illustrative embodiment, a system/apparatus
is provided. The system/apparatus may comprise one or more
processors and a memory coupled to the one or more processors. The
memory may comprise instructions which, when executed by the one or
more processors, cause the one or more processors to perform
various ones of, and combinations of, the operations outlined above
with regard to the method illustrative embodiment.
[0009] These and other features and advantages of the present
invention will be described in, or will become apparent to those of
ordinary skill in the art in view of, the following detailed
description of the example embodiments of the present
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The invention, as well as a preferred mode of use and
further objectives and advantages thereof, will best be understood
by reference to the following detailed description of illustrative
embodiments when read in conjunction with the accompanying
drawings, wherein:
[0011] FIG. 1 depicts an example of a traditional object storage
architecture;
[0012] FIG. 2 depicts an example of an embedded compute engine in
an object storage architecture;
[0013] FIG. 3 is an example diagram of a distributed data
processing system in which aspects of the illustrative embodiments
may be implemented;
[0014] FIG. 4 is an example block diagram of a computing device in
which aspects of the illustrative embodiments may be
implemented;
[0015] FIG. 5 depicts a functional block diagram of a storlet
scheduler mechanism that improves computation performance and
reduces workload on the storage area network (SAN) powered object
storage units in accordance with one illustrative embodiment;
and
[0016] FIG. 6 depicts a flowchart of the operation performed by a
storlet scheduler mechanism in optimizing object storage workflow
that leverages underlying storage area network value adds in
accordance with an illustrative embodiment.
DETAILED DESCRIPTION
[0017] As discussed previously, a storlet (embedded compute engine
in an object storage) architecture comprises a software engine
present within the nodes, the nodes being a storage node or a proxy
node. When an end user wants the storlet architecture to perform a
computation, the end user has to frame a computational algorithm
and deploy or pass the computational algorithm to the embedded
software engine as a normal object PUT operation. The storlet
architecture does not require any additional client or compute node
to perform analysis of the data. That is, in the storlet
architecture, the storage nodes/proxy nodes themselves act as
compute node and return computational results back to the end user.
The storlet architecture also uses virtual machines, such as
Linux.TM. containers, Docker, ZeroVM, or the like, deployed on the
storage nodes/proxy nodes to perform the computation tasks.
[0018] Currently, datacenters comprise a plurality of servers that
are coupled to shared pools of object storage devices via a storage
area network (SAN) that is a dedicated high-speed network. A SAN
moves storage resources off a common user network and reorganizes
them into an independent, high-performance network. This allows
each server to access the shared pools of object storage devices as
if the shared storage were a storage drive directly attached to the
server. When a host wants to access a storage device on the SAN,
the host sends out a block based access request for the storage
device. Value additions provided by SAN storage include, but are
not limited to, thin provisioning, tiering, compression,
deduplication, Flash acceleration support, high/low speed disk rpm
support, Active/Active replication, encryption, or the like.
[0019] The storlet engine deployed within these object storage
units which helps in preparing the hardware resources computation
ready typically comprises a virtualization unit (may be Linux.TM.
containers, Docker, ZeroVM, or the like) and few middleware's
(software units) that helps decide the computation operation to be
performed by the virtualization unit based on the user deployed
computational algorithm. The signal flow graph corresponding to
storlet engine is as follows: [0020] user deploys computational
algorithm (PUT operation) [0021] parse for syntax errors [0022]
determine the computation operation (may be arithmetic or any
specialized operation such as txt to pdf, editing jpg, or the like)
[0023] determine the node to be used for instantiating
virtualization unit [0024] pass the computation operation to
virtualization unit [0025] virtualization unit pulls/reads/writes
data based on the steps defined in the computational algorithm
[0026] return results back to the user with code
(success/failure).
[0027] The traditional storlet engine treats each node
participating in the object storage cluster equally (irrespective
of their type, model, operating system flavor, version,
virtualization technology, etc.) and identifies the nodes unique
using network address (IP address). With this kind of model, the
storlet engine execution steps of: determining the node to be used
for instantiating virtualization unit and passing the computation
operation to virtualization unit, is achieved in two ways: [0028]
1. Virtualization unit may be instantiated on the node that
comprises maximum data required for fulfilling that particular
computational algorithm. [0029] 2. Virtualization unit may be
instantiated on the node that comprises maximum available hardware
resources.
[0030] Therefore, these scaled-out object storage units are built
using commodity hardware mostly reusing the existing
infrastructure. In a large enterprise environment, raw storage is
primarily derived from a plurality of SAN appliances, either single
or multi-vendor appliances, maintained as a pool and supplied to
compute units depending upon their necessity. Without taking into
consideration the features of the object storage units, such as
hardware accelerators, disk speed, tiering, compression,
deduplication, optimization efficiency, or the like, scheduling
based on the above described two scheduling techniques proves to be
less efficient in the case of a multi-SAN storage environment.
[0031] For example, in a scenario where in the storage servers used
to build an object storage system are powered by volumes derived
from multi-vendor SAN units (nodes 1, 2, and 3 being powered by
storage derived from Hewlett-Packard.RTM. (HP.RTM.) SAN unit, nodes
4, 5, and 6 being powered by International Business Machines
Corporation (IBM.RTM.) SAN unit, and nodes 7, 8, and 9 are powered
by Hitachi.RTM. SAN unit, these SAN storage units contain different
built-in features which helps the SAN storage units process faster
in case of certain workloads. For example, HP.RTM. SAN volumes
contain special hardware accelerators which provides faster
processing of compression workloads, IBM.RTM. SAN volumes contain
special ASIC (Application Specific Integrated Chip) which provides
interaction with FLASH disks and provides faster processing of
arithmetic workloads, Hitachi.RTM. SAN volumes contain special
software which provides for faster image processing. In this
scenario, assume an end user has deployed a computational algorithm
that is identified as an arithmetic operation and both the IBM.RTM.
and HP.RTM. storage units reported a similar resource availability.
As current storlet engines treat all nodes as equal, the storlet
engine receiving the computational algorithm may assign this
computational algorithm to a node powered by HP storage unit for
handling, which in turn results in poor performance in terms of
increased time for processing and increased load on storage unit.
That is, if the features of the SAN storage units were taken into
consideration, the storlet engine would have selected the node
powered by IBM storage unit for this workload which could deliver
better results than any other nodes because the IBM.RTM. SAN
volumes units contain special ASIC (Application Specific Integrated
Chip) which provides interaction with FLASH disks and provides
faster processing of arithmetic workloads.
[0032] The core reason for the above mentioned problem is lack of
framework and middleware's which helps storlet engine (embedded
compute infrastructure within object storage) to understand the
underlying storage features and select the nodes based on the
workloads (computational algorithm input) which can be accelerated
using a particular SAN platform.
[0033] In order to perform object storage workflow optimization
that leverages underlying storage features or value adds built into
the SAN storage devices, such as thin provisioning, tiering,
compression, deduplication, Flash acceleration support, high/low
speed disk rpm support, Active/Active replication, encryption, or
the like, the illustrative embodiments storlet scheduler mechanism
installs a daemon on each infrastructure participating in the
object storage cluster. This daemon collects the SAN storage
features powering the storage nodes as well as the role executed by
each node in the SAN storage (i.e. proxy or storage). Each daemon
exports the collected information to the storlet scheduler
mechanism. Using the collected storage information, for each node,
the storlet scheduler mechanism identifies the underlying storage
features along with the role served by the node (i.e. proxy or
storage). For example, the information collected for a first node
may reveal the SAN capabilities of: thin provisioning, tiering,
compression, deduplication, and proxy node. As another example, the
information collected for a second node may reveal the SAN
capabilities of: replication, encryption, a disk speed of 7200 rpm,
and storage node.
[0034] Responsive to receiving a deployed computational algorithm
from an end user, the storlet scheduler mechanism parses the
computational algorithm to identify the operations required within
the computational algorithm. Utilizing the identified operations
from the computational algorithm and a set of predefined rules for
operations, the storlet scheduler mechanism identifies a class of
each operation such as encryption, seismic processing, mobile/code
render operations, compress and store, or the like. With the class
of the operation identified, the storlet scheduler mechanism
determines a category of the computational algorithm based on the
identified class from a pre-programmed table of categories and
value adds. Examples of category classification of deployed
computational algorithm by the proposed middleware may include:
[0035] Computation operations (Encrypt an object).fwdarw.Encryption
category [0036] Computation operations (Mobile
development).fwdarw.Developer category [0037] Computation
operations (Seismic data processing).fwdarw.Arithmetic category
[0038] Computation operations (Compress and Store).fwdarw.Direct
Memory category
[0039] For each category within the pre-programmed table of
categories and value adds, the value adds associated with each
category are preferred SAN features, such as encryption, Flash
acceleration, deduplication, compression, or the like. Utilizing
the identified category and value adds associated with the
computational algorithm, the storlet scheduler mechanism searches
the storage features associated with each node for features that
best match the value adds associated with the identified category
of the computational algorithm. Responsive to determining a best
match node, the storlet scheduler mechanism schedules the
computational algorithm to be executed on the best match node.
[0040] Thus, the mechanisms of the illustrative embodiments provide
for a storlet scheduler mechanism that improves computation
performance and reduces workload on the object storage units in a
multi-vendor commodity SAN powered object storage environment by
performing specific workflow changes in the embedded compute engine
according to SAN features, such as thin provisioning, tiering,
compression, deduplication, Flash acceleration support, high/low
speed disk rpm support, Active/Active replication, encryption, or
the like.
[0041] Having given an overview of operations in accordance with
one illustrative embodiment, before beginning the discussion of the
various aspects of the illustrative embodiments, it should first be
appreciated that throughout this description the term "mechanism"
will be used to refer to elements of the present invention that
perform various operations, functions, and the like. A "mechanism,"
as the term is used herein, may be an implementation of the
functions or aspects of the illustrative embodiments in the form of
an apparatus, a procedure, or a computer program product. In the
case of a procedure, the procedure is implemented by one or more
devices, apparatus, computers, data processing systems, or the
like. In the case of a computer program product, the logic
represented by computer code or instructions embodied in or on the
computer program product is executed by one or more hardware
devices in order to implement the functionality or perform the
operations associated with the specific "mechanism." Thus, the
mechanisms described herein may be implemented as specialized
hardware, software executing on general-purpose hardware, software
instructions stored on a medium such that the instructions are
readily executable by specialized or general-purpose hardware, a
procedure or method for executing the functions, or a combination
of any of the above.
[0042] The present description and claims may make use of the terms
"a," "at least one of," and "one or more of" with regard to
particular features and elements of the illustrative embodiments.
It should be appreciated that these terms and phrases are intended
to state that there is at least one of the particular feature or
element present in the particular illustrative embodiment, but that
more than one can also be present. That is, these terms/phrases are
not intended to limit the description or claims to a single
feature/element being present or require that a plurality of such
features/elements be present. To the contrary, these terms/phrases
only require at least a single feature/element with the possibility
of a plurality of such features/elements being within the scope of
the description and claims.
[0043] Moreover, it should be appreciated that the use of the term
"engine," if used herein with regard to describing embodiments and
features of the invention, is not intended to be limiting of any
particular implementation for accomplishing and/or performing the
actions, steps, processes, etc., attributable to and/or performed
by the engine. An engine may be, but is not limited to, software,
hardware and/or firmware or any combination thereof that performs
the specified functions including, but not limited to, any use of a
general and/or specialized processor in combination with
appropriate software loaded or stored in a machine readable memory
and executed by the processor. Further, any name associated with a
particular engine is, unless otherwise specified, for purposes of
convenience of reference and not intended to be limiting to a
specific implementation. Additionally, any functionality attributed
to an engine may be equally performed by multiple engines,
incorporated into and/or combined with the functionality of another
engine of the same or different type, or distributed across one or
more engines of various configurations.
[0044] In addition, it should be appreciated that the following
description uses a plurality of various examples for various
elements of the illustrative embodiments to further illustrate
example implementations of the illustrative embodiments and to aid
in the understanding of the mechanisms of the illustrative
embodiments. These examples intended to be non-limiting and are not
exhaustive of the various possibilities for implementing the
mechanisms of the illustrative embodiments. It will be apparent to
those of ordinary skill in the art in view of the present
description that there are many other alternative implementations
for these various elements that may be utilized in addition to, or
in replacement of, the examples provided herein without departing
from the spirit and scope of the present invention.
[0045] Thus, the illustrative embodiments may be utilized in many
different types of data processing environments. In order to
provide a context for the description of the specific elements and
functionality of the illustrative embodiments, FIGS. 3 and 4 are
provided hereafter as example environments in which aspects of the
illustrative embodiments may be implemented. It should be
appreciated that FIGS. 3 and 4 are only examples and are not
intended to assert or imply any limitation with regard to the
environments in which aspects or embodiments of the present
invention may be implemented. Many modifications to the depicted
environments may be made without departing from the spirit and
scope of the present invention.
[0046] FIG. 3 depicts a pictorial representation of an example
distributed data processing system in which aspects of the
illustrative embodiments may be implemented. Distributed data
processing system 300 may include a network of computers in which
aspects of the illustrative embodiments may be implemented. The
distributed data processing system 300 contains at least one
network 302, which is the medium used to provide communication
links between various devices and computers connected together
within distributed data processing system 300. The network 302 may
include connections, such as wire, wireless communication links, or
fiber optic cables.
[0047] In the depicted example, server 304 and server 306 are
connected to network 302 along with storage unit 308 and storage
unit 309. In addition, clients 310, 312, and 314 are also connected
to network 302. These clients 310, 312, and 314 may be, for
example, personal computers, network computers, or the like. In the
depicted example, server 304 provides data, such as boot files,
operating system images, and applications to the clients 310, 312,
and 314. Clients 310, 312, and 314 are clients to server 304 in the
depicted example. Distributed data processing system 300 may
include additional servers, clients, and other devices not
shown.
[0048] In the depicted example, distributed data processing system
300 is the Internet with network 302 representing a worldwide
collection of networks and gateways that use the Transmission
Control Protocol/Internet Protocol (TCP/IP) suite of protocols to
communicate with one another. At the heart of the Internet is a
backbone of high-speed data communication lines between major nodes
or host computers, consisting of thousands of commercial,
governmental, educational, and other computer systems that route
data and messages. Of course, the distributed data processing
system 300 may also be implemented to include a number of different
types of networks, such as for example, an intranet, a local area
network (LAN), a wide area network (WAN), or the like. As stated
above, FIG. 3 is intended as an example, not as an architectural
limitation for different embodiments of the present invention, and
therefore, the particular elements shown in FIG. 3 should not be
considered limiting with regard to the environments in which the
illustrative embodiments of the present invention may be
implemented.
[0049] As shown in FIG. 3, one or more of the computing devices,
e.g., storage unit 308 and storage unit 309, may be specifically
configured to implement a storlet scheduler mechanism. The
configuring of the computing device may comprise the providing of
application specific hardware, firmware, or the like to facilitate
the performance of the operations and generation of the outputs
described herein with regard to the illustrative embodiments. The
configuring of the computing device may also, or alternatively,
comprise the providing of software applications stored in one or
more storage devices and loaded into memory of a computing device,
such as server 104, for causing one or more hardware processors of
the computing device to execute the software applications that
configure the processors to perform the operations and generate the
outputs described herein with regard to the illustrative
embodiments. Moreover, any combination of application specific
hardware, firmware, software applications executed on hardware, or
the like, may be used without departing from the spirit and scope
of the illustrative embodiments.
[0050] It should be appreciated that once the computing device is
configured in one of these ways, the computing device becomes a
specialized computing device specifically configured to implement
the mechanisms of the illustrative embodiments and is not a
general-purpose computing device. Moreover, as described hereafter,
the implementation of the mechanisms of the illustrative
embodiments improves the functionality of the computing device and
provides a useful and concrete result that facilitates improving
computation performance and reducing workload on object storage
units in a multi-vendor commodity SAN powered object storage
environment by performing specific workflow changes in the embedded
compute engine according to SAN storage value additions.
[0051] As noted above, the mechanisms of the illustrative
embodiments utilize specifically configured computing devices, or
data processing systems, to perform the operations for improving
computation performance and reducing workload on object storage
units in a multi-vendor commodity SAN powered object storage
environment by performing specific workflow changes in the embedded
compute engine according to SAN storage value additions. These
computing devices, or data processing systems, may comprise various
hardware elements that are specifically configured, either through
hardware configuration, software configuration, or a combination of
hardware and software configuration, to implement one or more of
the systems/subsystems described herein. FIG. 4 is a block diagram
of an example data processing system in which aspects of the
illustrative embodiments may be implemented. Data processing system
400 is an example of a computer, such as server 304, storage unit
308, and client 310 in FIG. 3, in which computer usable code or
instructions implementing the processes for illustrative
embodiments of the present invention may be located.
[0052] In the depicted example, data processing system 400 employs
a hub architecture including north bridge and memory controller hub
(NB/MCH) 402 and south bridge and input/output (I/O) controller hub
(SB/ICH) 404. Processing unit 406, main memory 408, and graphics
processor 410 are connected to NB/MCH 402. Graphics processor 410
may be connected to NB/MCH 402 through an accelerated graphics port
(AGP).
[0053] In the depicted example, local area network (LAN) adapter
412 connects to SB/ICH 404. Audio adapter 416, keyboard and mouse
adapter 420, modem 422, read only memory (ROM) 424, hard disk drive
(HDD) 426, CD-ROM drive 430, universal serial bus (USB) ports and
other communication ports 432, and PCI/PCIe devices 434 connect to
SB/ICH 404 through bus 438 and bus 440. PCI/PCIe devices may
include, for example, Ethernet adapters, add-in cards, and PC cards
for notebook computers. PCI uses a card bus controller, while PCIe
does not. ROM 424 may be, for example, a flash basic input/output
system (BIOS).
[0054] HDD 426 and CD-ROM drive 430 connect to SB/ICH 404 through
bus 440. HDD 426 and CD-ROM drive 430 may use, for example, an
integrated drive electronics (IDE) or serial advanced technology
attachment (SATA) interface. Super I/O (SIO) device 436 may be
connected to SB/ICH 404.
[0055] An operating system runs on processing unit 406. The
operating system coordinates and provides control of various
components within the data processing system 400 in FIG. 4. As a
client, the operating system may be a commercially available
operating system such as Microsoft.RTM. Windows 7.RTM.. An
object-oriented programming system, such as the Java.TM.
programming system, may run in conjunction with the operating
system and provides calls to the operating system from Java.TM.
programs or applications executing on data processing system
400.
[0056] As a server, data processing system 400 may be, for example,
an IBM eServer.TM. System p.RTM. computer system, Power.TM.
processor based computer system, or the like, running the Advanced
Interactive Executive (AIX.RTM.) operating system or the LINUX.RTM.
operating system. Data processing system 400 may be a symmetric
multiprocessor (SMP) system including a plurality of processors in
processing unit 406. Alternatively, a single processor system may
be employed.
[0057] Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on
storage devices, such as HDD 426, and may be loaded into main
memory 408 for execution by processing unit 406. The processes for
illustrative embodiments of the present invention may be performed
by processing unit 406 using computer usable program code, which
may be located in a memory such as, for example, main memory 408,
ROM 424, or in one or more peripheral devices 426 and 430, for
example.
[0058] A bus system, such as bus 438 or bus 440 as shown in FIG. 4,
may be comprised of one or more buses. Of course, the bus system
may be implemented using any type of communication fabric or
architecture that provides for a transfer of data between different
components or devices attached to the fabric or architecture. A
communication unit, such as modem 422 or network adapter 412 of
FIG. 4, may include one or more devices used to transmit and
receive data. A memory may be, for example, main memory 408, ROM
424, or a cache such as found in NB/MCH 402 in FIG. 4.
[0059] As mentioned above, in some illustrative embodiments the
mechanisms of the illustrative embodiments may be implemented as
application specific hardware, firmware, or the like, application
software stored in a storage device, such as HDD 426 and loaded
into memory, such as main memory 408, for execution by one or more
hardware processors, such as processing unit 406, or the like. As
such, the computing device shown in FIG. 4 becomes specifically
configured to implement the mechanisms of the illustrative
embodiments and specifically configured to perform the operations
and generate the outputs described hereafter with regard to a
storlet scheduler mechanism that improves computation performance
and reduces workload on the object storage units in a multi-vendor
commodity SAN powered object storage environment by performing
specific workflow changes in the embedded compute engine according
to SAN storage value additions.
[0060] Those of ordinary skill in the art will appreciate that the
hardware in FIGS. 3 and 4 may vary depending on the implementation.
Other internal hardware or peripheral devices, such as flash
memory, equivalent non-volatile memory, or optical disk drives and
the like, may be used in addition to or in place of the hardware
depicted in FIGS. 3 and 4. In addition, the processes of the
illustrative embodiments may be applied to a multiprocessor data
processing system, other than the SMP system mentioned previously,
without departing from the spirit and scope of the present
invention.
[0061] Moreover, the data processing system 400 may take the form
of any of a number of different data processing systems including
client computing devices, server computing devices, a tablet
computer, laptop computer, telephone or other communication device,
a personal digital assistant (PDA), or the like. In some
illustrative examples, data processing system 400 may be a portable
computing device that is configured with flash memory to provide
non-volatile memory for storing operating system files and/or
user-generated data, for example. Essentially, data processing
system 400 may be any known or later developed data processing
system without architectural limitation.
[0062] FIG. 5 depicts a functional block diagram of a storlet
scheduler mechanism that improves computation performance and
reduces workload on the storage area network (SAN) powered object
storage units in accordance with one illustrative embodiment.
Object storage architecture 500 comprises a plurality of diverse
infrastructures 502a-502n that are accessible by client devices
504a-504n via load balancer 506. Each of infrastructures 502a-502n
comprises a set of node groups 508 and 510. First node group 508 is
proxy nodes that are used for distributed load handling/request
handling from client devices 504a-504n into the storage namespace.
Second node group 510 is storage nodes, i.e. the storage namespace,
that are responsible for writing to the disks or storage
subsystems. Further, each of the infrastructures 502a-502n comprise
software engine 512. As discussed previously, embedded compute
engine 512 performs any computation or analysis required by client
devices 504a-504n. A user of client devices 504a-504n has to frame
the computational algorithm to perform the computation or analysis
and has to deploy or pass the computational algorithm to embedded
compute engine 512 at the time the request is submitted. Embedded
compute engine 512 then sends the results of the computation back
to the requesting user of client device 504a-504n.
[0063] In accordance with the illustrative embodiments, in order to
perform object storage workflow optimization that leverages
underlying SAN storage value adds, object storage architecture 500
also comprises storlet scheduler mechanism 514. In operation,
storlet scheduler mechanism 514 initially installs daemon 516 on
each infrastructure participating in the object storage cluster.
Daemon 516 collects SAN storage feature information, such as thin
provisioning, tiering, compression, deduplication, Flash
acceleration support, high/low speed disk rpm support,
Active/Active replication, encryption, or the like, powering of the
infrastructure on which the daemon 516 is installed as well as the
role executed by each node (i.e. proxy or storage) on the
infrastructure. Each daemon 516 exports the collected information
from the node to storlet scheduler mechanism 514. Using the
collected storage information, for each node, storlet scheduler
mechanism 514 identifies the underlying storage features (value add
features) along with the role served by the node (i.e. proxy or
storage), which is then stored in storage 518 as node features 520.
For example, the information collected from infrastructure 502a
reveals the SAN features of: thin provisioning, tiering, and
compression. The information from node 502b reveals the SAN
features of: deduplication and Flash acceleration. The information
from node 502c reveals the SAN features of: Active/Active
replication, encryption, and high-speed disk. The information from
node 502d reveals the SAN features of: thin provisioning, tiering,
and compression. The information from node 502n reveals the SAN
features of: deduplication and Flash acceleration. Again, each
infrastructure may contain both proxy nodes and storage nodes.
Therefore, for each node on the infrastructure, storlet scheduler
mechanism 514 stores the operating system type, operating system
version, virtualization technology, and hardware associated with
that node. As an example, if infrastructure 502a comprises two
proxy nodes and two storage nodes, storlet scheduler mechanism 514
would store the following in node features 520:
[0064] IN: 502a, Node 1: Proxy, thin provisioning, tiering, and
compression.
[0065] IN: 502a, Node 2: Proxy, thin provisioning, tiering, and
compression.
[0066] IN: 502a, Node 3: Store, thin provisioning, tiering, and
compression.
[0067] IN: 502a, Node 4: Store, thin provisioning, tiering, and
compression.
[0068] With the value add features of each node collected and
stored, storlet scheduler mechanism 514 waits for a computational
algorithm from a user of one of client devices 504a-504n.
Responsive to receiving a computational algorithm from a user,
storlet scheduler mechanism 514 determines whether the
computational algorithm has an identified category. If not, then
storlet scheduler mechanism 514 parses the computational algorithm
to identify the operations required within the computational
algorithm. Utilizing the identified operations from the
computational algorithm and a set of predefined rules for
operations, storlet scheduler mechanism 514 identifies a class of
each operation such as encryption, seismic processing, mobile/code
render operations, compress and store, or the like. With the class
of the operation identified, storlet scheduler mechanism 514
determines a category of the computational algorithm based on the
identified class from a pre-programmed table of categories and
value adds. Examples of category classification of a deployed
computational algorithm by the proposed middleware may include:
[0069] Computation operations (Encrypt an object).fwdarw.Encryption
category [0070] Computation operations (Mobile
development).fwdarw.Developer category [0071] Computation
operations (Seismic data processing).fwdarw.Arithmetic category
[0072] Computation operations (Compress and Store).fwdarw.Direct
Memory category
[0073] For each category within the pre-programmed table of
categories and value adds, the value adds associated with each
category are an optimal set of SAN features, such as preferred SAN
features of encryption, Flash acceleration, deduplication,
compression, or the like. Utilizing the identified category and
value adds associated with the computational algorithm, storlet
scheduler mechanism 514 searches the node features 520 associated
with each node for features that match the value adds associated
with the identified category of the computational algorithm. The
result of the search may result in a ranked list of nodes
indicating a percentage that each node matches the value adds
associated with the identified category of the computational
algorithm.
[0074] Responsive to determining one or more exact matches of the
value adds associated with the identified category of the
computational algorithm and a node from the node features 520,
storlet scheduler mechanism 514 randomly selects a node from one of
the one or more exact matches and determines whether resources
associated with the node are immediately available. If the
resources are immediately available, then storlet scheduler
mechanism 514 issues the computational algorithm to the node for
execution. If the resources are not immediately available, then
storlet scheduler mechanism 514 selects another of the one or more
exact matches and determines whether resources associated with the
node are immediately available and repeats the process until all
nodes have been checked. If none of the one or more exact match
nodes has resources immediately available, storlet scheduler
mechanism 514 continues to check each node until one of the nodes
has resources available, at which time storlet scheduler mechanism
514 issues the computational algorithm to the node for
execution.
[0075] Responsive to determining only one exact match of the value
adds associated with the identified category of the computational
algorithm and a node from the node features 520, storlet scheduler
mechanism 514 determines whether resources associated with the node
are immediately available. If the resources are immediately
available, then storlet scheduler mechanism 514 issues the
computational algorithm to the node for execution. If the resources
are not immediately available, then storlet scheduler mechanism 514
waits for a predetermined time period for the resources to become
available. If the resources become available within the
predetermined time period, storlet scheduler mechanism 514 issues
the computational algorithm to the node for execution.
[0076] If the resources fail to become available within the
predetermined time period, then storlet scheduler mechanism 514 may
select a next node in the ranked list and determine whether
resources associated with the node are immediately available. If
the resources are immediately available, then storlet scheduler
mechanism 514 issues the computational algorithm to the node for
execution. If the resources are not immediately available, then
storlet scheduler mechanism 514 waits for a predetermined time
period for the resources to become available. If the resources
become available within the predetermined time period, storlet
scheduler mechanism 514 issues the computational algorithm to the
node for execution. Storlet scheduler mechanism 514 repeats the
process for each node in the ranked list until the computational
algorithm is issued to a node for execution.
[0077] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0078] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0079] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0080] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0081] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0082] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0083] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0084] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0085] FIG. 6 depicts a flowchart of the operation performed by a
storlet scheduler mechanism in optimizing object storage workflow
that leverages underlying SAN feature value adds in accordance with
an illustrative embodiment. As the operation begins, the storlet
scheduler mechanism receives a computational algorithm from a user
of one of a set of client devices (step 602). Responsive to
receiving a computational algorithm from a user, the storlet
scheduler mechanism determines whether the computational algorithm
has an identified category (step 604). If at step 604 the
computational algorithm fails to have an identified category, the
storlet scheduler mechanism parses the computational algorithm to
identify the operations required within the computational algorithm
(step 606). Utilizing the identified operations from the
computational algorithm and a set of predefined rules for
operations, the storlet scheduler mechanism identifies a class of
each operation (step 608) such as encryption, seismic processing,
mobile/code render operations, compress and store, or the like. The
storlet scheduler mechanism then determines a category of the
computational algorithm based on the identified class from a
pre-programmed table of categories and value adds (step 610). For
each category within the pre-programmed table of categories and
value adds, the value adds associated with each category are an
optimal set of SAN features, such as preferred SAN features of
encryption, Flash acceleration, deduplication, compression, or the
like.
[0086] From step 610 or if at step 604 the computational algorithm
has an identified category, the storlet scheduler mechanism
utilizes the identified category and value adds associated with the
computational algorithm to search node features associated with
each node for features that match the value adds associated with
the identified category of the computational algorithm (step 612).
The node features associated with each node may be gathered by the
storlet scheduler mechanism receiving and storing SAN feature
information powering each infrastructure participating in the
object storage cluster as well as the role executed by each node on
the infrastructure (i.e. proxy or storage). The information is
collected by individual daemons installed on each infrastructure.
Each daemon exports the collected information from the node to the
storlet scheduler mechanism. Using the collected storage
information, for each node, the storlet scheduler mechanism
identifies the underlying SAN features (value add features) along
with the role served by the node (i.e. proxy or storage).
Therefore, for each node on the infrastructure, the storlet
scheduler mechanism stores associated SAN features, such as thin
provisioning, tiering, compression, deduplication, Flash
acceleration support, high/low speed disk rpm support,
Active/Active replication, encryption, or the like. The result of
the search may result in a ranked list of nodes indicating a
percentage that each node matches the value adds associated with
the identified category of the computational algorithm.
[0087] The storlet scheduler mechanism then determines whether
there are any exact matches of the value adds associated with the
identified category of the computational algorithm one or more
nodes from the node features (step 614). If at step 614 there are
one or more exact matches, then the storlet scheduler mechanism
determines whether there is more than one exact match (step 616).
If at step 616 there is more than one exact match, the storlet
scheduler mechanism randomly selects a node from one of the more
than one exact match (step 618) and determines whether resources
associated with the node are immediately available (step 620). If
at step 620 the resources associated with the node are immediately
available, then the storlet scheduler mechanism issues the
computational algorithm to the node for execution (step 622) with
the operation terminating thereafter. If at step 620 the resources
are not immediately available, the operation returns to step 618 to
select another node from then more than one exact matches. This
process is repeated for instances where there is more than one
exact match due to performing the computational algorithm on an
exact match being preferred.
[0088] If at step 616 there is only one exact match, the storlet
scheduler mechanism determines whether resources associated with
the node are immediately available (step 624). If at step 624 the
resources are immediately available, then the operation proceeds to
step 622. If at step 624 the resources are not immediately
available, then the storlet scheduler mechanism waits for a
predetermined time period for the resources to become available
(step 626). If at step 626 the resources become available within
the predetermined time period, the storlet scheduler mechanism
issues the computational algorithm to the node for execution (step
622). If at step 626 the resources fail to become available within
the predetermined time period or if at step 614 there are no exact
matches, then the storlet scheduler mechanism selects a first/next
best match node in the ranked list (step 628) and proceeds/returns
to step 624. The storlet scheduler mechanism repeats the process
for each node in the ranked list until the computational algorithm
is issued to a node for execution. If the end of the list is
reached, the storlet scheduler mechanism returns to the top of the
list.
[0089] Thus, the illustrative embodiments provide mechanisms for
improving computation performance and reducing workload on the
object storage units in a multi-vendor commodity object storage
environment by performing specific workflow changes in the embedded
compute engine according to SAN feature value additions such as
thin provisioning, tiering, compression, deduplication, Flash
acceleration support, high/low speed disk rpm support,
Active/Active replication, encryption, or the like.
[0090] As noted above, it should be appreciated that the
illustrative embodiments may take the form of an entirely hardware
embodiment, an entirely software embodiment or an embodiment
containing both hardware and software elements. In one example
embodiment, the mechanisms of the illustrative embodiments are
implemented in software or program code, which includes but is not
limited to firmware, resident software, microcode, etc.
[0091] A data processing system suitable for storing and/or
executing program code will include at least one processor coupled
directly or indirectly to memory elements through a system bus. The
memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories
which provide temporary storage of at least some program code in
order to reduce the number of times code must be retrieved from
bulk storage during execution.
[0092] Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the
system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the
data processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modems and Ethernet cards
are just a few of the currently available types of network
adapters.
[0093] The description of the present invention has been presented
for purposes of illustration and description, and is not intended
to be exhaustive or limited to the invention in the form disclosed.
Many modifications and variations will be apparent to those of
ordinary skill in the art without departing from the scope and
spirit of the described embodiments. The embodiment was chosen and
described in order to best explain the principles of the invention,
the practical application, and to enable others of ordinary skill
in the art to understand the invention for various embodiments with
various modifications as are suited to the particular use
contemplated. The terminology used herein was chosen to best
explain the principles of the embodiments, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
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